3 citations · 9 across the 8 of their papers we have counts for
8 papers
Learning 3D Editing without Paired Supervision via Generative Prior Distillation
Hao Wen, Weibin Yun, Hongxing Fan +4
Instruction-guided 3D editing is essential for interactive content creation, yet it faces a significant bottleneck: the severe scarcity of high-quality paired training data. Existi…
Reasoning-Driven Amodal Completion: Collaborative Agents and Perceptual Evaluation
Hongxing Fan, Shuyu Zhao, Jiayang Ao +1
Amodal completion, the task of inferring invisible object parts, faces significant challenges in maintaining semantic consistency and structural integrity. Prior progressive approa…
InterMoE: Individual-Specific 3D Human Interaction Generation via Dynamic Temporal-Selective MoE
Lipeng Wang, Hongxing Fan, Haohua Chen +2
Generating high-quality human interactions holds significant value for applications like virtual reality and robotics. However, existing methods often fail to preserve unique indiv…
Multi-Agent Amodal Completion: Direct Synthesis with Fine-Grained Semantic Guidance
Hongxing Fan, Lipeng Wang, Haohua Chen +3
Amodal completion, generating invisible parts of occluded objects, is vital for applications like image editing and AR. Prior methods face challenges with data needs, generalizatio…
From Parts to Whole: A Unified Reference Framework for Controllable Human Image Generation
Zehuan Huang, Hongxing Fan, Lipeng Wang +1
Recent advancements in controllable human image generation have led to zero-shot generation using structural signals (e.g., pose, depth) or facial appearance. Yet, generating human…
Assessment of Multimodal Large Language Models in Alignment with Human Values
Zhelun Shi, Zhipin Wang, Hongxing Fan +7
Large Language Models (LLMs) aim to serve as versatile assistants aligned with human values, as defined by the principles of being helpful, honest, and harmless (hhh). However, in…
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Chaochao Lu, Chen Qian, Guodong Zheng +33
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents. However, there is still a wide ga…
ChEF: A Comprehensive Evaluation Framework for Standardized Assessment of Multimodal Large Language Models
Zhelun Shi, Zhipin Wang, Hongxing Fan +4
Multimodal Large Language Models (MLLMs) have shown impressive abilities in interacting with visual content with myriad potential downstream tasks. However, even though a list of b…